dtormoen

dtormoen / neural-open.nvim

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A smart picker for Snacks.nvim that trains a neural network with your file picking preferences.

lua machine-learning neovim neovim-plugin snacks-nvim
33
1
100% credibility
Found Feb 28, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Lua
AI Summary

Neural Open is a Neovim plugin that enhances file picking by learning from user selections to rank files using neural networks, weighted scoring, or simple sums, adapting to individual navigation patterns.

How It Works

1
🔍 Discover Neural Open

You hear about a clever file finder for your code editor that learns your favorite files and paths over time.

2
📥 Add it to your editor

You follow simple steps to include it in your editor setup so it's ready to use.

3
🚀 Open the smart finder

Hit a quick key combo and instantly see files sorted by what you likely want next.

4
🧠 Pick your files

Choose the file you need, and it quietly notes your choice to get smarter.

5
📈 See it adapt

After a few days of use, suggestions match your real workflow perfectly.

🎉 Fly through your projects

Navigate files lightning-fast, feeling like the editor knows your every move.

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Star Growth

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AI-Generated Review

What is neural-open.nvim?

Neural-open.nvim is a Lua plugin turning Snacks.nvim into a smart picker for files that learns your navigation habits. It ranks results using a neural network trained on your selections, blending fuzzy matching with context like open buffers, directory proximity, frecency, trigram similarity, and file transitions—much like a snacks smart picker on steroids. Ships with pre-trained weights for instant smarts, persisting adaptations across sessions via simple Lua config.

Why is it gaining traction?

Stands out by personalizing rankings online without setup hassle, outperforming generic fuzzy tools on repeated patterns in large repos. Toggle algorithms (neural net default, classic weights, or naive sum), preview score breakdowns live, and benchmark sub-ms latency on 100k files. Pre-trained defaults mimic real workflows, hooking devs seeking github smart commits-style intuition for everyday file jumps.

Who should use this?

Neovim devs grinding large codebases—think backend teams hopping schemas → controllers → tests, or frontend folks cycling components → utils → hooks. Perfect for anyone on Snacks.nvim tired of scrolling past irrelevant files in fuzzy pickers, especially in monorepos where proximity and recency matter. Skip if you prefer Telescope's simplicity.

Verdict

Worth adding for Snacks.nvim users chasing smarter file navigation—10 stars and 1.0% credibility reflect early maturity, but thorough docs, isolated tests, and reset commands make it stable to experiment with. Give it days to adapt; fallback algos keep it reliable.

(198 words)

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